{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c17c940a-b7cf-4414-92bc-1dfa87e4b240",
   "metadata": {},
   "source": [
    "# Burgers' Equation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "60b3f8b4-6d5b-4797-b1e8-b366e717a82e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import jax\n",
    "import jax.numpy as jnp\n",
    "\n",
    "import optax\n",
    "from flax import linen as nn\n",
    "\n",
    "import sys\n",
    "import os\n",
    "\n",
    "import time\n",
    "\n",
    "# Add /src to path\n",
    "path_to_src = os.path.abspath(os.path.join(os.getcwd(), '../../../../src'))\n",
    "if path_to_src not in sys.path:\n",
    "    sys.path.append(path_to_src)\n",
    "\n",
    "from KAN import KAN\n",
    "from PIKAN import *\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dca1f133-8abf-44fe-9f98-2cf13efb29ed",
   "metadata": {},
   "source": [
    "### Collocation Points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1a606415-9b0b-44d7-94fd-dcbad3ee8807",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate Collocation points for PDE\n",
    "N = 2**12\n",
    "collocs = jnp.array(sobol_sample(np.array([0,-1]), np.array([1,1]), N)) # (4096, 2)\n",
    "\n",
    "# Generate Collocation points for BCs\n",
    "N = 2**6\n",
    "\n",
    "BC1_colloc = jnp.array(sobol_sample(np.array([0,-1]), np.array([0,1]), N)) # (64, 2)\n",
    "BC1_data = - jnp.sin(np.pi*BC1_colloc[:,1]).reshape(-1,1) # (64, 1)\n",
    "\n",
    "BC2_colloc = jnp.array(sobol_sample(np.array([0,-1]), np.array([1,-1]), N)) # (64, 2)\n",
    "BC2_data = jnp.zeros(BC2_colloc.shape[0]).reshape(-1,1) # (64, 1)\n",
    "\n",
    "BC3_colloc = jnp.array(sobol_sample(np.array([0,1]), np.array([1,1]), N)) # (64, 2)\n",
    "BC3_data = jnp.zeros(BC3_colloc.shape[0]).reshape(-1,1) # (64, 1)\n",
    "\n",
    "# Create lists for BCs\n",
    "bc_collocs = [BC1_colloc, BC2_colloc, BC3_colloc]\n",
    "bc_data = [BC1_data, BC2_data, BC3_data]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eefadd88-07ae-407c-88cb-d46dd35158f4",
   "metadata": {},
   "source": [
    "### Loss Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "56f78031-ad4b-42b9-8428-3eb8c1858833",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pde_loss(params, collocs, state):\n",
    "    # Eq. parameter\n",
    "    v = jnp.array(0.01/jnp.pi, dtype=float)\n",
    "    \n",
    "    # Define the model function\n",
    "    variables = {'params' : params, 'state' : state}\n",
    "    \n",
    "    def u(vec_x):\n",
    "        y, spl = model.apply(variables, vec_x)\n",
    "        return y\n",
    "        \n",
    "    # Physics Loss Terms\n",
    "    u_t = gradf(u, 0, 1)  # 1st order derivative of t\n",
    "    u_x = gradf(u, 1, 1)  # 1st order derivative of x\n",
    "    u_xx = gradf(u, 1, 2) # 2nd order derivative of x\n",
    "    \n",
    "    # Residual\n",
    "    pde_res = u_t(collocs) + u(collocs)*u_x(collocs) - v*u_xx(collocs)\n",
    "    \n",
    "    return pde_res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a58c2b06-fd40-4f88-a98d-0fc1fd7eb34e",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Training Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c16886fe-031d-43a7-bcc7-89855d9b9f52",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize model\n",
    "layer_dims = [2, 8, 8, 1]\n",
    "model = KAN(layer_dims=layer_dims, k=3, const_spl=False, const_res=False, add_bias=True, grid_e=0.05)\n",
    "variables = model.init(jax.random.PRNGKey(0), jnp.ones([1, 2]))\n",
    "\n",
    "# Define learning rates for scheduler\n",
    "lr_vals = dict()\n",
    "lr_vals['init_lr'] = 0.001\n",
    "lr_vals['scales'] = {0 : 1.0}\n",
    "\n",
    "# Define epochs for grid extension, along with grid sizes\n",
    "grid_extend = {0 : 3}\n",
    "\n",
    "# Define global loss weights\n",
    "glob_w = [jnp.array(1.0, dtype=float), jnp.array(1.0, dtype=float), jnp.array(1.0, dtype=float), jnp.array(1.0, dtype=float)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "882dfa41-cd58-4eb0-8f61-7048b1e1440c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: Performing grid update\n",
      "Total Time: 521.1513569355011 s\n",
      "Average time per iteration: 0.0052 s\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 100000\n",
    "\n",
    "model, variables, train_losses = train_PIKAN(model, variables, pde_loss, collocs, bc_collocs, bc_data, glob_w=glob_w, \n",
    "                                             lr_vals=lr_vals, adapt_state=False, loc_w=None, nesterov=False, \n",
    "                                             num_epochs=num_epochs, grid_extend=grid_extend, grid_adapt=[], \n",
    "                                             colloc_adapt={'epochs' : []})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4a6437fa-857b-4a9d-a389-640f82ee8bcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "L^2 Error = 13.5037%\n"
     ]
    }
   ],
   "source": [
    "# Draw reference values\n",
    "ref = np.load('../../External Data/Burgers.npz')\n",
    "\n",
    "N_t, N_x = 100, 256\n",
    "\n",
    "t = np.linspace(0.0, 1.0, N_t)\n",
    "x = np.linspace(-1.0, 1.0, N_x)\n",
    "T, X = np.meshgrid(t, x, indexing='ij')\n",
    "coords = np.stack([T.flatten(), X.flatten()], axis=1)\n",
    "\n",
    "output, _ = model.apply(variables, jnp.array(coords))\n",
    "baseline = np.array(output).reshape(N_t, N_x)\n",
    "\n",
    "l2err = jnp.linalg.norm(baseline-ref['usol'].T)/jnp.linalg.norm(ref['usol'].T)\n",
    "print(f\"L^2 Error = {l2err*100:.4f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7cbac6e3-bfca-47b0-94c2-a53d8c4b8d5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savez('../../Plots/data/eq3-base.npz', t=t, x=x, baseline=baseline, ref=ref['usol'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c7c47c9-8a93-49b6-a86a-070965573458",
   "metadata": {},
   "source": [
    "### Training Adaptive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "82b53959-ffdd-4d46-99ad-22f569c53fa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize model\n",
    "layer_dims = [2, 8, 8, 1]\n",
    "model = KAN(layer_dims=layer_dims, k=3, const_spl=False, const_res=False, add_bias=True, grid_e=0.05)\n",
    "variables = model.init(jax.random.PRNGKey(0), jnp.ones([1, 2]))\n",
    "\n",
    "# Define learning rates for scheduler\n",
    "lr_vals = dict()\n",
    "lr_vals['init_lr'] = 0.001\n",
    "lr_vals['scales'] = {0 : 1.0, 8_000 : 0.7, 20_000 : 0.7, 25_000 : 0.7, 50_000 : 0.8, 75_000 : 0.8, 85_000 : 0.6}\n",
    "\n",
    "# Define epochs for grid adaptation\n",
    "adapt_every = 300\n",
    "adapt_stop = 75000\n",
    "grid_adapt = [i * adapt_every for i in range(1, (adapt_stop // adapt_every) + 1)]\n",
    "\n",
    "# Define epochs for grid extension, along with grid sizes\n",
    "grid_extend = {0 : 3, 8000 : 8, 20_000 : 14}\n",
    "\n",
    "# Define global loss weights\n",
    "glob_w = [jnp.array(1.0, dtype=float), jnp.array(1.0, dtype=float), jnp.array(1.0, dtype=float), jnp.array(1.0, dtype=float)]\n",
    "\n",
    "# Initialize RBA weights\n",
    "loc_w = [jnp.ones((collocs.shape[0],1)), jnp.ones((BC1_colloc.shape[0],1)),\n",
    "         jnp.ones((BC2_colloc.shape[0],1)), jnp.ones((BC3_colloc.shape[0],1))]\n",
    "\n",
    "# Perform adaptive collocation point sampling\n",
    "colloc_adapt = dict({'M' : 2**17, 'k' : jnp.array(1.0, dtype=float), 'c' : jnp.array(1.0, dtype=float)})\n",
    "colloc_adapt['epochs'] = [50_000, 75_000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "64c890b3-903e-4012-89b7-a0e36bd4734c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: Performing grid update\n",
      "Epoch 8000: Performing grid update\n",
      "Epoch 20000: Performing grid update\n",
      "Total Time: 1411.4006035327911 s\n",
      "Average time per iteration: 0.0141 s\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 100000\n",
    "\n",
    "model, variables, train_losses2 = train_PIKAN(model, variables, pde_loss, collocs, bc_collocs, bc_data, glob_w=glob_w, \n",
    "                                             lr_vals=lr_vals, adapt_state=True, loc_w=loc_w, nesterov=True, \n",
    "                                             num_epochs=num_epochs, grid_extend=grid_extend, grid_adapt=grid_adapt, \n",
    "                                             colloc_adapt=colloc_adapt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4f5a68f0-9464-4c80-bb3c-a71879f78ceb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "L^2 Error = 2.4296%\n"
     ]
    }
   ],
   "source": [
    "# Draw reference values\n",
    "ref = np.load('../../External Data/Burgers.npz')\n",
    "\n",
    "N_t, N_x = 100, 256\n",
    "\n",
    "t = np.linspace(0.0, 1.0, N_t)\n",
    "x = np.linspace(-1.0, 1.0, N_x)\n",
    "T, X = np.meshgrid(t, x, indexing='ij')\n",
    "coords = np.stack([T.flatten(), X.flatten()], axis=1)\n",
    "\n",
    "output, _ = model.apply(variables, jnp.array(coords))\n",
    "adaptive = np.array(output).reshape(N_t, N_x)\n",
    "\n",
    "l2err = jnp.linalg.norm(adaptive-ref['usol'].T)/jnp.linalg.norm(ref['usol'].T)\n",
    "print(f\"L^2 Error = {l2err*100:.4f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b56790ab-26eb-48ef-a3f5-120cd9fe4c84",
   "metadata": {},
   "source": [
    "### Final Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fe328f2c-f2b9-419c-aff0-d143b4c21110",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[3m                                  KAN Summary                                   \u001b[0m\n",
      "┏━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┓\n",
      "┃\u001b[1m \u001b[0m\u001b[1mpath    \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mmodule  \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1minputs     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1moutputs    \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mstate      \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mparams      \u001b[0m\u001b[1m \u001b[0m┃\n",
      "┡━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━┩\n",
      "│          │ KAN      │ \u001b[2mfloat32\u001b[0m[40… │ -           │             │ bias_0:      │\n",
      "│          │          │             │ \u001b[2mfloat32\u001b[0m[40… │             │ \u001b[2mfloat32\u001b[0m[8]   │\n",
      "│          │          │             │ - -         │             │ bias_1:      │\n",
      "│          │          │             │ \u001b[2mfloat32\u001b[0m[8,… │             │ \u001b[2mfloat32\u001b[0m[8]   │\n",
      "│          │          │             │   -         │             │ bias_2:      │\n",
      "│          │          │             │ \u001b[2mfloat32\u001b[0m[8,… │             │ \u001b[2mfloat32\u001b[0m[1]   │\n",
      "│          │          │             │   -         │             │              │\n",
      "│          │          │             │ \u001b[2mfloat32\u001b[0m[1,… │             │ \u001b[1m17 \u001b[0m\u001b[1;2m(68 B)\u001b[0m    │\n",
      "├──────────┼──────────┼─────────────┼─────────────┼─────────────┼──────────────┤\n",
      "│ layers_0 │ KANLayer │ \u001b[2mfloat32\u001b[0m[40… │ -           │ grid:       │ c_basis:     │\n",
      "│          │          │             │ \u001b[2mfloat32\u001b[0m[40… │ \u001b[2mfloat32\u001b[0m[16… │ \u001b[2mfloat32\u001b[0m[16,… │\n",
      "│          │          │             │ -           │             │ c_res:       │\n",
      "│          │          │             │ \u001b[2mfloat32\u001b[0m[8,… │ \u001b[1m160 \u001b[0m\u001b[1;2m(640 B)\u001b[0m │ \u001b[2mfloat32\u001b[0m[16]  │\n",
      "│          │          │             │             │             │ c_spl:       │\n",
      "│          │          │             │             │             │ \u001b[2mfloat32\u001b[0m[16]  │\n",
      "│          │          │             │             │             │              │\n",
      "│          │          │             │             │             │ \u001b[1m128 \u001b[0m\u001b[1;2m(512 B)\u001b[0m  │\n",
      "├──────────┼──────────┼─────────────┼─────────────┼─────────────┼──────────────┤\n",
      "│ layers_1 │ KANLayer │ \u001b[2mfloat32\u001b[0m[40… │ -           │ grid:       │ c_basis:     │\n",
      "│          │          │             │ \u001b[2mfloat32\u001b[0m[40… │ \u001b[2mfloat32\u001b[0m[64… │ \u001b[2mfloat32\u001b[0m[64,… │\n",
      "│          │          │             │ -           │             │ c_res:       │\n",
      "│          │          │             │ \u001b[2mfloat32\u001b[0m[8,… │ \u001b[1m640 \u001b[0m\u001b[1;2m(2.6 \u001b[0m   │ \u001b[2mfloat32\u001b[0m[64]  │\n",
      "│          │          │             │             │ \u001b[1;2mKB)\u001b[0m         │ c_spl:       │\n",
      "│          │          │             │             │             │ \u001b[2mfloat32\u001b[0m[64]  │\n",
      "│          │          │             │             │             │              │\n",
      "│          │          │             │             │             │ \u001b[1m512 \u001b[0m\u001b[1;2m(2.0 KB)\u001b[0m │\n",
      "├──────────┼──────────┼─────────────┼─────────────┼─────────────┼──────────────┤\n",
      "│ layers_2 │ KANLayer │ \u001b[2mfloat32\u001b[0m[40… │ -           │ grid:       │ c_basis:     │\n",
      "│          │          │             │ \u001b[2mfloat32\u001b[0m[40… │ \u001b[2mfloat32\u001b[0m[8,… │ \u001b[2mfloat32\u001b[0m[8,6] │\n",
      "│          │          │             │ -           │             │ c_res:       │\n",
      "│          │          │             │ \u001b[2mfloat32\u001b[0m[1,… │ \u001b[1m80 \u001b[0m\u001b[1;2m(320 B)\u001b[0m  │ \u001b[2mfloat32\u001b[0m[8]   │\n",
      "│          │          │             │             │             │ c_spl:       │\n",
      "│          │          │             │             │             │ \u001b[2mfloat32\u001b[0m[8]   │\n",
      "│          │          │             │             │             │              │\n",
      "│          │          │             │             │             │ \u001b[1m64 \u001b[0m\u001b[1;2m(256 B)\u001b[0m   │\n",
      "├──────────┼──────────┼─────────────┼─────────────┼─────────────┼──────────────┤\n",
      "│\u001b[1m \u001b[0m\u001b[1m        \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1m        \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1m           \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1m      Total\u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1m880 \u001b[0m\u001b[1;2m(3.5 \u001b[0m\u001b[1m  \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1m721 \u001b[0m\u001b[1;2m(2.9 KB)\u001b[0m\u001b[1m \u001b[0m│\n",
      "│\u001b[1m          \u001b[0m│\u001b[1m          \u001b[0m│\u001b[1m             \u001b[0m│\u001b[1m             \u001b[0m│\u001b[1m \u001b[0m\u001b[1;2mKB)\u001b[0m\u001b[1m        \u001b[0m\u001b[1m \u001b[0m│\u001b[1m              \u001b[0m│\n",
      "└──────────┴──────────┴─────────────┴─────────────┴─────────────┴──────────────┘\n",
      "\u001b[1m                                                                                \u001b[0m\n",
      "\u001b[1m                        Total Parameters: 1,601 \u001b[0m\u001b[1;2m(6.4 KB)\u001b[0m\u001b[1m                        \u001b[0m\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tabulate_fn = nn.tabulate(model, jax.random.PRNGKey(11))\n",
    "\n",
    "table = tabulate_fn(collocs)\n",
    "print(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d8c205b1-c6e8-422b-8722-81fe6696a3aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.pcolormesh(T, X, np.abs(ref['usol'].T-adaptive), shading='auto', cmap='Spectral_r') #\n",
    "plt.colorbar()\n",
    "\n",
    "plt.title(\"Absolute Error for Burgers' Equation\")\n",
    "plt.xlabel('t')\n",
    "\n",
    "plt.ylabel('x')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d6401ed2-5188-469e-8c5e-10900be9eb32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.plot(np.array(train_losses2), label='Train Loss', marker='o', color='blue', markersize=1)\n",
    "\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training Loss Over Epochs')\n",
    "plt.yscale('log')  # Set y-axis to logarithmic scale\n",
    "\n",
    "plt.legend()\n",
    "plt.grid(True, which='both', linestyle='--', linewidth=0.5) \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3ef65e4b-bb26-475d-b07c-bbca6df50d65",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savez('../../Plots/data/eq3-adapt.npz', t=t, x=x, adaptive=adaptive, ref=ref['usol'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ad4ba32-612a-4c91-8628-ab9eda1a00cd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
